Tumors and lesions are types of pathological anatomies characterized by abnormal growth of tissue resulting from the uncontrolled, progressive multiplication of cells, while serving no physiological function.
A non-invasive method for pathological anatomy treatment is external beam radiation therapy. In one type of external beam radiation therapy, an external radiation source is used to direct a sequence of x-ray beams at a tumor site from multiple angles, with the patient positioned so the tumor is at the center of rotation (isocenter) of the beam. As the angle of the radiation source is changed, every beam passes through the tumor site, but passes through a different area of healthy tissue on its way to the tumor. As a result, the cumulative radiation dose at the tumor is high and the average radiation dose to healthy tissue is low. The term radiotherapy refers to a procedure in which radiation is applied to a target region for therapeutic, rather than necrotic, purposes. The amount of radiation utilized in radiotherapy treatment sessions is typically about an order of magnitude smaller, as compared to the amount used in a radiosurgery session. Radiotherapy is typically characterized by a low dose per treatment (e.g., 100-200 centiGray (cGy)), short treatment times (e.g., 10 to 30 minutes per treatment) and hyperfractionation (e.g., 30 to 45 days of treatment). For convenience, the term “radiation treatment” is used herein to mean radiosurgery and/or radiotherapy unless otherwise noted by the magnitude of the radiation.
Conventional isocentric radiosurgery systems (e.g., the Gamma Knife) use forward treatment planning. That is, a medical physicist determines the radiation dose to be applied to a tumor and then calculates how much radiation will be absorbed by critical structures and other healthy tissue. There is no independent control of the two dose levels, for a given number of beams, because the volumetric energy density at any given distance from the isocenter is a constant, no matter where the isocenter is located.
Inverse planning, in contrast to forward planning, allows the medical physicist to independently specify the minimum tumor dose and the maximum dose to other healthy tissues, and lets the treatment planning software select the direction, distance, and total number and energy of the beams. Conventional treatment planning software packages are designed to import 3-D images from a diagnostic imaging source, for example, computerized x-ray tomography (CT) scans. CT is able to provide an accurate three-dimensional model of a volume of interest (e.g., skull or other tumor bearing portion of the body) generated from a collection of CT slices and, thereby, the volume requiring treatment can be visualized in three dimensions.
During inverse planning, a volume of interest (VOI) is used to delineate structures to be targeted or avoided with respect to the administered radiation dose. That is, the radiation source is positioned in a sequence calculated to localize the radiation dose into a VOI that as closely as possible conforms to the tumor requiring treatment, while avoiding exposure of nearby healthy tissue. Once the target (e.g., tumor) VOI has been defined, and the critical and soft tissue volumes have been specified, the responsible radiation oncologist or medical physicist specifies the minimum radiation dose to the target VOI and the maximum dose to normal and critical healthy tissue. The software then produces the inverse treatment plan, relying on the positional capabilities of the radiation treatment system, to meet the min/max dose constraints of the treatment plan.
FIG. 1 is a conceptual illustration of a graphical output of a treatment planning software displaying a slice of a CT image. The CT image is of a human chest region as viewed from the feet of a patient lying on his or her back, and includes the right lung, the left lung, and the spine region. The right lung contains a pathological anatomy (e.g., tumor, lesion, etc.) region targeted for radiation treatment and the spine region contains a critical anatomy, the spinal cord (surrounded by the vertebral body), to be avoided by radiation because of the spinal cord's proximity to the pathological anatomy. The treatment planning software enables the generation of a critical region contour around the spinal cord, a target (i.e., pathological anatomy) region contour around the pathological anatomy, and a corresponding dose isocontour on the displayed CT slice. Conventionally, a user manually delineates points (e.g., some of the dots on the contour lines of FIG. 1) on the display that is used by the treatment planning software to generate the corresponding contours. While this may seem an easy task, such matching is difficult due the 3-dimensional nature and irregularities of the pathological and normal anatomies.
The two principal requirements for an effective radiation treatment system are homogeneity and conformality. Homogeneity is the uniformity of the radiation dose over the volume of the target (e.g., pathological anatomy such as a tumor, lesion, vascular malformation, etc.) characterized by a dose volume histogram (DVH). An ideal DVH for the pathological anatomy would be a rectangular function as illustrated in FIG. 2, where the dose is 100 percent of the prescribed dose over the volume of the pathological anatomy and zero elsewhere. A desirable DVH for a critical region would have the profile illustrated in FIG. 3, where the volume of the critical anatomical structures receives as little of the prescribed dose as possible.
Conformality is the degree to which the radiation dose matches (conforms) to the shape and extent of the target (e.g., tumor) in order to avoid damage to critical adjacent structures. More specifically, conformality is a measure of the amount of prescription (Rx) dose (amount of dose applied) within a target VOI. Conformality may be measured using a conformality index (CI)=total volume at>=Rx dose/target volume at>=Rx dose. Perfect conformality results in a CI=1. With conventional radiotherapy treatment, using treatment planning software, a clinician identifies a dose isocontour for a corresponding VOI for application of a treatment dose (e.g., 3000 cGy).
As discussed above, in current inverse planning systems, the user manually sets constraints (e.g., minimum and maximum dose to critical and target regions) before planning. Optimization constraints can be very patient-specific, so that using the same constraints on different patients may lead to grossly different planning results. Based on a set of constraints defined by the operator, the quality of the treatment plan can be characterized with a DVH. If the resulting DVH is acceptable, the operator can then decide to proceed with the set of constraints that generated the acceptable DVH. If not, the operator would go through a process of modifying one or more of the optimization constraints to generate an acceptable DVH. Over time, a set of acceptable treatment plans can be collected for the patient to be referenced for future treatment plan development. Similarly, a library of acceptable treatment plans can be formed for a pathological anatomy in a given anatomical region. For example, when going through the process of developing a treatment plan for a pathological anatomy in the lung, the operator can reference a library of DVHs from acceptable plans and attempt to modify the optimization constraints to better conform the current DVH to the DVHs from the library. However, this can be a tedious and time-consuming process because the operator has to modify one or more optimization constraints manually, determine what the resulting DVH looks like, and continue to repeat the modification process until the DVH has the acceptable profile.